How to integrate Moderation API MCP with Pydantic AI

This guide walks you through connecting Moderation API to Pydantic AI using the Composio tool router. By the end, you'll have a working Moderation API agent that can review flagged content, approve safe posts, block repeat offenders, and manage your wordlists through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Moderation API account through Composio's Moderation API MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Moderation API is a content safety service for detection, human review, author management, and moderation actions. Use it to keep user-generated content safer with clear workflows and audit-ready decisions.

11 Tools

Introduction

This guide walks you through connecting Moderation API to Pydantic AI using the Composio tool router. By the end, you'll have a working Moderation API agent that can review flagged content, approve safe posts, block repeat offenders, and manage your wordlists through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a Moderation API account through Composio's Moderation API MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Moderation API with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Moderation API
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Moderation API workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

What is the Moderation API MCP server, and what's possible with it?

The Moderation API MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Moderation API account. It provides structured and secure access to your moderation workflows, review queues, actions, and wordlists, so your agent can evaluate content, submit items for review, inspect queues, resolve reviewed items, and apply moderation actions on your behalf.

  • Content policy evaluation: Have your agent evaluate content without storing it, then report policy findings and the recommended moderation action.
  • Content submission and review: Let the agent submit content for moderation so it can be retained and added to a review queue based on your project settings.
  • Review queue management: Direct your agent to check queue totals, list and filter moderation items, and mark reviewed items as resolved or pending.
  • Moderation action execution: Instruct your agent to view available moderation actions and apply the appropriate action to content or authors.
  • Wordlist management: Have your agent list wordlists, inspect their current words, and add or remove terms from an existing wordlist.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Moderation API
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Moderation API
  • MCPServerStreamableHTTP connects to the Moderation API MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Moderation API
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["moderationapi"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Moderation API tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
moderationapi_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[moderationapi_mcp],
    instructions=(
        "You are a Moderation API assistant. Use Moderation API tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Moderation API endpoint
  • The agent uses GPT-5 to interpret user commands and perform Moderation API operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Moderation API.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Moderation API API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Moderation API and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Moderation API
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["moderationapi"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    moderationapi_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[moderationapi_mcp],
        instructions=(
            "You are a Moderation API assistant. Use Moderation API tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Moderation API.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with Moderation API through Composio's Tool Router. With this setup, your agent can perform real Moderation API actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Moderation API for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
TOOLS

Supported Tools

Every Moderation API action and event your agent gets out of the box.

Evaluate Content Without Storage

Evaluate content without retaining it or adding it to a review queue, and return policy findings and the recommended moderation action.

Execute Moderation Action

Apply a discovered moderation action to content items or authors.

Get Account and Quota

Return the connected Moderation API account, plan, current project, and current quota values.

Get Review Queue

Return metadata and item counts for a review queue by ID.

Get Wordlist

Return one wordlist and its current words by ID.

List Moderation Actions

List moderation actions available to the organization, optionally restricted to a review queue.

List Review Queue Items

List and filter one page of moderation items in a review queue.

List Wordlists

List the organization's wordlists and their identifiers.

Modify Wordlist Words

Add words to or remove words from an existing wordlist.

Set Review Queue Item Status

Mark a review queue item resolved or return it to pending.

Submit Content for Moderation

Evaluate content and allow Moderation API to retain it and add it to a review queue according to the project's configuration.

FRAMEWORKS

How to build Moderation API MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Moderation API MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Moderation API MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Moderation API MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Moderation API MCP with Claude Cowork

Codex logo

Codex

Use Moderation API MCP with Codex

OpenClaw logo

OpenClaw

Use Moderation API MCP with OpenClaw

Hermes logo

Hermes

Use Moderation API MCP with Hermes

CLI logo

CLI

Use Moderation API MCP with CLI

Google ADK logo

Google ADK

Use Moderation API MCP with Google ADK

LangChain logo

LangChain

Use Moderation API MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Moderation API MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Moderation API MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Moderation API MCP with LlamaIndex

CrewAI logo

CrewAI

Use Moderation API MCP with CrewAI

FAQ

Frequently asked questions

With a standalone Moderation API MCP server, the agents and LLMs can only access a fixed set of Moderation API tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Moderation API and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. Pydantic AI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Moderation API tools.

Yes, absolutely. You can configure which Moderation API scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Moderation API data and credentials are handled as safely as possible.

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